Source-free domain adaptation. Privacy and memory limitations during adaptation may prevent the access of source data for training. Source-free approaches only use unlabeled target data network models pre-trained on the source data during adaptation. Kundu [1]} propose a UDA method which does not require information about the category-level gap between domains, and consists of a procurement stage in which a generative classifier equips the model to reject out-of-distribution samples, and a deployment stage, in which adversarial alignment is performed. Yang [2]} focuses on maintaining model performance on the source domain by a two stage method that consists of a local structure clustering stage and a domain attention stage that activates feature channels associated with each domain. Saltori [3]} propose a source-free UDA method for 3D object detection on PointRCNN [4]}, utilizing a tracking-based scoring system to evaluate the quality of pseudo-labels at different scales. As mentioned above, this method depends on the use of multiple frames for a single forward pass through the network.
